CMOS image sensors are typically formed as an array of pixels, where each pixel includes a photodetector that transforms incident light photons into current signals. Each pixel may also include other known elements, such as a reset switch, a signal amplifier, and output circuits that operate to set the exposure time of the photodetector and perform a read out indicative of light photons incident thereon. Where incident light is too high for the set exposure time of the pixel, the photodetector typically saturates.
FIG. 1 illustrates a prior art CMOS image sensor pixel array 100. Pixel array 100 is configured for column parallel readout and has a plurality of columns, each having a pixel 102 for each of a plurality of rows. In column parallel readout architecture, for each row, one pixel 102 in each column is read out and processed simultaneously. That is, pixels 102 of Row 0 are read out in parallel, then pixels 102 of Row 1 are read out in parallel, then pixels 102 of Row 2 are read out in parallel, and so on, until Row M is read out. Pixels 102 within each column connect to a column readout line 105, such that when a row is triggered for output, each pixel in that row outputs a signal to its associated column readout line 105, while outputs of other pixels in the column remain inactive. Array 100 is shown with one sample and hold element 104 for each column read out line 105. Sample and hold elements 104 and column read out lines 105 cooperate to provide a row-by-row read out of pixels 102. A second stage amplifier 106 connects to each of the sample and hold elements 104. All rows are typically output to form an image (also known as a frame).
CMOS image sensors are often used in applications in which both very bright and very dark conditions are encountered. A variety of techniques have been developed to improve the response of CMOS image sensors in a variety of light conditions. For example, U.S. Patent Publication No. 2004/0141075, entitled “Image Sensor Having Dual Automatic Exposure Control”, by Xiangchen Xu et al., is assigned to Omnivision Technologies, Inc. and is hereby incorporated by reference. Xu teaches that the gain and exposure time can be adjusted over a sequence of frames to compensate for varying light conditions. An adjustment in exposure time is determined by analyzing one frame and then used to make an adjustment for a subsequent frame. While such approach controls exposure times over a series of frames to adjust for bright and dark conditions, it does not result in an increase in the dynamic range of the image sensor for a particular frame. As is well known in the field of image sensors, the dynamic range is the ratio of the largest detectable signal to the smallest, which for a CMOS image sensor is often defined by the ratio of the largest non-saturating signal to the standard deviation of the noise under dark conditions.
U.S. Patent Publication No. 2009/0059048, entitled “Image Sensor with High Dynamic Range in Down-Sampling Mode”, by Xiaodong Luo et al, is also assigned to Omnivision Technologies, Inc. and is hereby incorporated by reference. Luo introduces a system and method to achieve a high dynamic range in a down-sampling operation mode by varying exposure times for different pixel rows and combining rows with different exposures, thus simultaneously reducing the vertical resolution and extending the dynamic range.
In down-sampling, a binning process is used to combine data from two or more pixels to increase a signal to noise ratio (SNR), and a high dynamic range (HDR) combination process is used to combine data from two or more pixels to increase dynamic range. In the binning process, all rows have the same exposure time, while in the HDR combination process, rows of pixels can have different exposure times.
A Bayer pattern, which is one of the most commonly used patterns for down-sampling, generates zigzag edges during both the HDR combination process and the binning process. Although corrective algorithms for these zigzag edges have been developed for use with the Bayer pattern, these corrective algorithms have certain disadvantages, such as reducing sharpness and resolution of output frames and increasing cost of image sensors. For example, a binning re-interpolation algorithm can partly smooth zigzag edges caused by the Bayer pattern, but with a sacrifice in sharpness and resolution of the resultant frame. Re-interpolation also becomes very expensive since more memory is necessary.